This paper presents an automated water body detection system in SAR images. It uses recursive Otsu thresholding for automatic threshold determination, adaptive thresholding, k-means clustering, and color-based segmentation for improved accuracy. This approach eliminates manual intervention, enabling near-real-time extraction of water bodies from extensive SAR data. The system holds promise for applications in environmental monitoring, disaster management, and resource assessment, enhancing efficiency and precision in water body identification. Due to differences in backscatter properties, the difficulty lies in manually adjusting the threshold for each SAR dataset. To address this issue, we propose an innovative approach that performs the segmentation using these four techniques: recursive Otsu thresholding, adaptive thresholding, color-based segmentation, and k-means clustering. The ideal threshold for water body recognition is automatically determined via the recursive Otsu thresholding approach, which does not require human interaction. This technique refines the threshold selection process by leveraging the normalized between-class variance, ensuring precise water body identification.